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Optimal MRP parameters for a single item inventory with random replenishment lead time, POQ policy and service level constraint

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  • Louly, Mohamed-Aly
  • Dolgui, Alexandre

Abstract

This study deals with Material Requirement Planning (MRP) software parameterization under uncertainties. The actual lead time has random deviations; so it can be considered as a random variable. MRP approach with Periodic Order Quantity (POQ) policy is considered. The aim is to find the optimal MRP time phasing corresponding to each periodicity of the POQ policy. This is a crucial issue in supply planning with MRP approach because inappropriate planned lead times under lead time uncertainties invariably lead to large and costly inventories or insufficient customer service levels. The proposed model and algorithms minimize the sum of the setup and holding costs while satisfying a constraint on the service level. Our approach does not need to employ the commonly used normal probability distributions. Instead, its originality is in finding a closed form of the objective function, valid for any probability distribution of the actual lead times.

Suggested Citation

  • Louly, Mohamed-Aly & Dolgui, Alexandre, 2013. "Optimal MRP parameters for a single item inventory with random replenishment lead time, POQ policy and service level constraint," International Journal of Production Economics, Elsevier, vol. 143(1), pages 35-40.
    • Handle: RePEc:eee:proeco:v:143:y:2013:i:1:p:35-40
    DOI: 10.1016/j.ijpe.2011.02.009
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    Cited by:

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    2. Faicel Hnaien & Alexandre Dolgui & Desheng Dash Wu, 2016. "Single-period inventory model for one-level assembly system with stochastic lead times and demand," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 186-203, January.
    3. Slama, Ilhem & Ben-Ammar, Oussama & Thevenin, Simon & Dolgui, Alexandre & Masmoudi, Faouzi, 2022. "Stochastic program for disassembly lot-sizing under uncertain component refurbishing lead times," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1183-1198.
    4. Ben-Ammar, Oussama & Bettayeb, Belgacem & Dolgui, Alexandre, 2019. "Optimization of multi-period supply planning under stochastic lead times and a dynamic demand," International Journal of Production Economics, Elsevier, vol. 218(C), pages 106-117.
    5. Hnaien, Faicel & Afsar, Hasan Murat, 2017. "Robust single-item lot-sizing problems with discrete-scenario lead time," International Journal of Production Economics, Elsevier, vol. 185(C), pages 223-229.
    6. Altendorfer, Klaus, 2015. "Influence of lot size and planned lead time on service level and inventory for a single-stage production system with advance demand information and random required lead times," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 478-488.
    7. Klaus Altendorfer & Thomas Felberbauer & Herbert Jodlbauer, 2018. "Effects of forecast errors on optimal utilisation in aggregate production planning with stochastic customer demand," Papers 1812.00773, arXiv.org.
    8. Jian Li & Lu Liu & Hao Hu & Qiuhong Zhao & Libin Guo, 2018. "An Inventory Model for Deteriorating Drugs with Stochastic Lead Time," IJERPH, MDPI, vol. 15(12), pages 1-20, December.
    9. Manuel Díaz-Madroñero & Josefa Mula & Mariano Jiménez & David Peidro, 2017. "A rolling horizon approach for material requirement planning under fuzzy lead times," International Journal of Production Research, Taylor & Francis Journals, vol. 55(8), pages 2197-2211, April.
    10. Ben-Ammar, Oussama & Dolgui, Alexandre & Wu, Desheng Dash, 2018. "Planned lead times optimization for multi-level assembly systems under uncertainties," Omega, Elsevier, vol. 78(C), pages 39-56.
    11. Heydari, Jafar & Mahmoodi, Mansour & Taleizadeh, Ata Allah, 2016. "Lead time aggregation: A three-echelon supply chain model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 89(C), pages 215-233.
    12. Milne, R. John & Mahapatra, Santosh & Wang, Chi-Tai, 2015. "Optimizing planned lead times for enhancing performance of MRP systems," International Journal of Production Economics, Elsevier, vol. 167(C), pages 220-231.

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